Journal ArticleDOI
Variations and extension of the convex–concave procedure
Thomas Lipp,Stephen Boyd +1 more
TLDR
This work investigates the convex–concave procedure, a local heuristic that utilizes the tools of convex optimization to find local optima of difference of conveX (DC) programming problems, and generalizes the algorithm to include vector inequalities.Abstract:
We investigate the convex–concave procedure, a local heuristic that utilizes the tools of convex optimization to find local optima of difference of convex (DC) programming problems. The class of DC problems includes many difficult problems such as the traveling salesman problem. We extend the standard procedure in two major ways and describe several variations. First, we allow for the algorithm to be initialized without a feasible point. Second, we generalize the algorithm to include vector inequalities. We then present several examples to demonstrate these algorithms.read more
Citations
More filters
Journal ArticleDOI
Majorization-Minimization Algorithms in Signal Processing, Communications, and Machine Learning
TL;DR: An overview of the majorization-minimization (MM) algorithmic framework, which can provide guidance in deriving problem-driven algorithms with low computational cost and is elaborated by a wide range of applications in signal processing, communications, and machine learning.
Journal ArticleDOI
Content-Centric Sparse Multicast Beamforming for Cache-Enabled Cloud RAN
TL;DR: This paper presents a content-centric transmission design in a cloud radio access network by incorporating multicasting and caching, and reformulates an equivalent sparse multicast beamforming (SBF) problem, transformed into the difference of convex programs and effectively solved using the convex-concave procedure algorithms.
Journal ArticleDOI
Reconfigurable Intelligent Surfaces: Principles and Opportunities
TL;DR: A comprehensive overview of the state-of-the-art on RISs, with focus on their operating principles, performance evaluation, beamforming design and resource management, applications of machine learning to RIS-enhanced wireless networks, as well as the integration of RISs with other emerging technologies.
Journal ArticleDOI
A Framework of Robust Transmission Design for IRS-Aided MISO Communications With Imperfect Cascaded Channels
TL;DR: In this paper, the robust beamforming based on the imperfect cascaded BS-IRS-user channels at the transmitter was studied, where the transmit power minimization problems were formulated subject to the worst-case rate constraints under the bounded CSI error model, and the rate outage probability constraint under the statistical CSI estimation model, respectively.
References
More filters
Journal ArticleDOI
Maximum likelihood from incomplete data via the EM algorithm
Book
Statistical Analysis with Missing Data
TL;DR: This work states that maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse and large-Sample Inference Based on Maximum Likelihood Estimates is likely to be high.
Book
Numerical Optimization
Jorge Nocedal,Stephen J. Wright +1 more
TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
Book ChapterDOI
Reducibility Among Combinatorial Problems
TL;DR: The work of Dantzig, Fulkerson, Hoffman, Edmonds, Lawler and other pioneers on network flows, matching and matroids acquainted me with the elegant and efficient algorithms that were sometimes possible.
Reducibility Among Combinatorial Problems.
TL;DR: Throughout the 1960s I worked on combinatorial optimization problems including logic circuit design with Paul Roth and assembly line balancing and the traveling salesman problem with Mike Held, which made me aware of the importance of distinction between polynomial-time and superpolynomial-time solvability.